Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	21,127
2	New Jersey	20,128
3	Arizona	19,417
4	Louisiana	19,080
5	Rhode Island	16,795
6	Massachusetts	16,429
7	District of Columbia	15,861
8	Florida	15,716
9	Mississippi	14,060
10	Delaware	13,790
11	Connecticut	13,433
12	Alabama	13,304
13	South Carolina	13,131
14	Maryland	12,864
15	Illinois	12,788
16	Iowa	12,143
17	Georgia	12,087
18	Nebraska	11,654
19	Texas	11,382
20	Nevada	11,235
21	Tennessee	10,911
22	Arkansas	10,780
23	Utah	10,427
24	California	9,698
25	North Carolina	9,361
26	Virginia	8,947
27	South Dakota	8,887
28	Indiana	8,473
29	Pennsylvania	8,184
30	Michigan	8,149
31	Idaho	8,076
32	Minnesota	8,069
33	New Mexico	7,981
34	Wisconsin	7,748
35	Kansas	7,633
36	Colorado	6,913
37	North Dakota	6,444
38	Oklahoma	6,332
39	Ohio	6,315
40	Washington	6,288
41	Missouri	5,598
42	Kentucky	5,092
43	New Hampshire	4,550
44	Wyoming	3,642
45	Puerto Rico	3,586
46	Oregon	3,374
47	Alaska	2,948
48	West Virginia	2,746
49	Maine	2,712
50	Montana	2,311
51	Vermont	2,144
52	Hawaii	940

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Florida	554
2	Arizona	454
3	Nevada	434
4	Alabama	419
5	Texas	386
6	Mississippi	367
7	South Carolina	347
8	Idaho	342
9	Tennessee	341
10	Georgia	320
11	Louisiana	319
12	Arkansas	246
13	California	233
14	Utah	228
15	North Carolina	206
16	Oklahoma	188
17	Iowa	187
18	Wisconsin	155
19	North Dakota	149
20	Missouri	142
21	New Mexico	142
22	Delaware	129
23	Ohio	128
24	Washington	126
25	Kansas	123
26	Maryland	121
27	Kentucky	119
28	Indiana	116
29	Puerto Rico	112
30	Virginia	111
31	Minnesota	102
32	Montana	100
33	Nebraska	97
34	Illinois	94
35	Alaska	90
36	Colorado	90
37	Oregon	90
38	District of Columbia	79
39	South Dakota	79
40	Michigan	76
41	Wyoming	70
42	West Virginia	67
43	Pennsylvania	62
44	Rhode Island	48
45	Massachusetts	43
46	New York	39
47	Connecticut	24
48	New Jersey	20
49	New Hampshire	18
50	Maine	16
51	Hawaii	14
52	Vermont	10

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,767
2	New York	1,653
3	Connecticut	1,233
4	Massachusetts	1,221
5	Rhode Island	934
6	District of Columbia	818
7	Louisiana	754
8	Michigan	637
9	Illinois	590
10	Maryland	557
11	Pennsylvania	552
12	Delaware	537
13	Mississippi	452
14	Indiana	418
15	Arizona	375
16	Colorado	304
17	Georgia	292
18	New Hampshire	291
19	Minnesota	279
20	New Mexico	271
21	Ohio	267
22	Alabama	262
23	Iowa	250
24	Virginia	237
25	Florida	227
26	South Carolina	220
27	Nevada	210
28	Washington	198
29	California	194
30	Missouri	188
31	Nebraska	159
32	North Carolina	157
33	Kentucky	153
34	Wisconsin	146
35	Texas	136
36	South Dakota	131
37	North Dakota	123
38	Tennessee	121
39	Arkansas	118
40	Oklahoma	113
41	Kansas	107
42	Vermont	89
43	Maine	87
44	Utah	75
45	Idaho	67
46	Oregon	61
47	Puerto Rico	55
48	West Virginia	55
49	Wyoming	41
50	Montana	34
51	Alaska	21
52	Hawaii	16

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	13
2	South Carolina	8
3	Mississippi	6
4	Alabama	5
5	Florida	5
6	Texas	4
7	District of Columbia	3
8	Louisiana	3
9	Nevada	3
10	Arkansas	2
11	California	2
12	Georgia	2
13	Massachusetts	2
14	New Jersey	2
15	Tennessee	2
16	Connecticut	1
17	Idaho	1
18	Illinois	1
19	Indiana	1
20	Iowa	1
21	Kentucky	1
22	Maryland	1
23	Michigan	1
24	Minnesota	1
25	Nebraska	1
26	New Mexico	1
27	North Carolina	1
28	Ohio	1
29	Oklahoma	1
30	Pennsylvania	1
31	South Dakota	1
32	Utah	1
33	Virginia	1
34	Washington	1
35	Wyoming	1
36	Alaska	0
37	Colorado	0
38	Delaware	0
39	Hawaii	0
40	Kansas	0
41	Maine	0
42	Missouri	0
43	Montana	0
44	New Hampshire	0
45	New York	0
46	North Dakota	0
47	Oregon	0
48	Puerto Rico	0
49	Rhode Island	0
50	Vermont	0
51	West Virginia	0
52	Wisconsin	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	136,742	1	99
Lake	Tennessee	99,629	2	99
Lee	Arkansas	96,985	3	99
Dakota	Nebraska	93,329	4	99
Buena Vista	Iowa	89,246	5	99
Davidson	Tennessee	24,450	122	96
Richland	South Carolina	14,263	399	87
Orange	California	9,135	796	74
York	South Carolina	7,944	934	70
Pierce	Washington	4,526	1539	51

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,902	1	99
Randolph	Georgia	3,688	2	99
Terrell	Georgia	3,399	3	99
Early	Georgia	3,042	4	99
Emporia city	Virginia	2,806	5	99
Richland	South Carolina	255	666	78
Davidson	Tennessee	239	698	77
Orange	California	156	952	69
Pierce	Washington	128	1074	65
York	South Carolina	50	1678	46

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.